In this blog I talk about some of the personal programming I do as a hobby. By trade I'm a Java and database developer but I've dabbled in Haskell, front-end development, etc. I'm currently working as an architect but still enjoy getting my fingers dirty in code!

Thursday, February 05, 2015

Genetic evolution of a neural network

In a previous post I was trying the LambdaNet library for neural networks, training one with a function my own little human brain designed. But can we make the network learn without knowing the actual algorithm?

As a implementation of a genetic algorithm, I use simple-genetic-algorithm, that is, ahem, simple to use compared to some other packages. I've modified it, though, to use MonadRandom instead of having to thread through the RandomGen, so if you want to run the code make sure to take the version from my fork in the MonadRandom branch, until these changes are released by the maintainer on Hackage.

To use a genetic algorithm, we need two things: a representation of the structure we want to evolve that is suitable for the genetic transformations, and the implementation of these transformations (crossover and mutation) themselves.

Note that we need a LayerDefinition to rebuild the network from the Vectors. Currently each layer of the network has the same definition and the algorithm does NOT evolve this structure, only the weights and biases.

We're going to keep that information along with some Worlds that we use for fitness testing:

Then we can implement the Chromosome class. Crossover takes the average of all weights and biases of the two parents, mutation changes one value randomly. Of course other implementations could be found.

For the fitness function we calculate the fitness for each given world and average it. I'm not trying to be too clever with that code.
For each world we run the food searching algorithm from each corners, and evaluate how far we are from the target, and if we reached it how long it took us. So networks that find the food will always rank higher than the ones who don't, and the quicker among them will rank higher again.

Programming is very interesting and creative thing if you do it with love. Your blog code helps a lot to beginners to learn programming from basic to advance level. I really love this blog because I learn a lot from here and this process is still continuing. Love from Pro Programmer

Evolution strategies could be more suitable for certain types of neural network:https://forum.processing.org/two/discussion/23422/a-simple-way-to-optimizeI also had this to say recently:https://groups.google.com/forum/#!topic/artificial-general-intelligence/4aKEE0gGGoA